Abstract
This paper describes the development of an adaptive control system for an outdoor mobile robot. The adaptive control system is composed of an environment recognition system using a self-organizing map and hybrid-neural network controllers based on neural networks. The environment recognition system can recognize the environment in which the robot travels and can switch the hybrid-neural network controller. The hybrid-neural network controllers are tuned by experimental results for each environment. To evaluate the performance of target tracking and vibration suppression, an experiment using the wheeled mobile robot, “Zaurus”, was conducted in rough terrain. As a result, our proposed method could show less oscillatory motion in rough terrain and performed better than a well tuned PID controller.
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